The effect of conditional cash transfers on tuberculosis incidence and mortality is determined by ethnoracial and socioeconomic factors: a cohort study of 54 million individuals in Brazil

Background. Conditional Cash Transfers (CCT) are the world’s most widely implemented interventions for poverty alleviation. Still, there is no solid evidence of the CCT effects on the reduction of the burden of Tuberculosis (TB) in marginalized and extremely vulnerable populations. We estimated the effect of the Bolsa Família Program (BFP), the largest CCT in the world, on TB incidence, mortality, and case-fatality rate using a nationwide cohort of 54.5 million individuals during a 12-year period in Brazil. Methods. We selected low-income individuals who entered in the 100 Million Brazilians Cohort and were linked to nationwide TB registries between 2004 to 2015, and compared BFP beneficiaries and non-beneficiaries using a quasi-experimental impact evaluation design. We employed inverse probability of treatment weighting (IPTW) multivariable Poisson regressions, adjusted for all relevant socioeconomic, demographic, and healthcare confounding variables - at individual and municipal level. Subsequently, we evaluated BFP effects for different subpopulations according to ethnoracial factors, wealth levels, sex, and age. We also performed several sensitivity and triangulation analyses to verify the robustness of the estimates. Findings. Exposure to BFP was associated with a large reduction in TB incidence in the low-income individuals under study (adjusted rate ratio [aRR]:0.59;95%CI:0.58–0.60) and mortality (aRR:0.69;95%CI:0.65–0.73). The strongest BFP effect was observed in Indigenous people both for TB incidence (aRR:0.37;95%CI:0.32–0.42), and mortality-aRR:0.35;95%CI:0.20–0.62), and in Black and Pardo people (Incidence-aRR:0.58;95%CI:0.57–0.59; Mortality -aRR:0.69;95%CI:0,64–0,73). BFP effects showed a clear gradient according to wealth levels and were considerably stronger among the extremely poor individuals for TB incidence (aRR:0.49, 95%CI:0.49–0.50) and mortality (aRR:0.60;95%CI:0.55–0.65). The BFP effects on case-fatality rates were also positive, however without statistical significance. Interpretation. CCT can strongly reduce TB incidence and mortality in extremely poor, Indigenous, Black and Pardo populations, and could significantly contribute to achieving the End TB Strategy targets and the TB-related Sustainable Development Goals.


Evidence before this study
The review identi ed some observational studies that explored the effect between CT programs and TB outcomes.The ndings suggest that CT and CCT programs have the potential to positively in uence TB prevention, diagnosis, and treatment.Cash transfers incentivize behavior change by providing nancial support to individuals or households who adhere to speci c healthrelated conditions, such as attending TB screening, completing treatment, or improving healthcare-seeking behaviour.Studies in low-and middle-income countries (LMICs) such as Ecuador, Argentina, Mexico, Brazil, Peru, India, Nigeria, Zimbabwe, Nepal, and Indonesia showed that CT programs were associated with improved TB outcomes, including increased treatment adherence and completion rates.Other observational studies indicate that CT effectively alleviates the economic burdens faced by TB-affected households.These costs, which often result from medical expenses, loss of income, and other related factors, can push affected households into poverty and hinder their access to proper TB care.Thus, these transfers help mitigate the catastrophic costs associated with TB, ensuring better access to treatment and reducing economic hardships for affected families.However, no study has ever systematically evaluated the effects of CCT programs in large cohorts of vulnerable individuals, and none has ever comprehensively estimated CCT effect according to gradients of poverty levels and ethnoracial differences, evaluating CCT impact on sequential TB burden indicators such as TB incidence, mortality, and case-fatality rate.

Added value of this study
To our knowledge, this is the largest evaluation ever attempted of the impact of a CCT program on an infectious disease, and in particular on TB incidence and mortality.Thanks to the unprecedented dimension of its cohort of individuals, it is the rst study to assess the effects of the world`s largest CCT with a unique level of granularity among socioeconomic vulnerable populations of an extremely unequal country of continental dimensions such as Brazil.We looked in particular at these effects across different ethnoracial groups, as well as levels of poverty, sex and age.Our results show that CCT programs, such as the Bolsa Família Program, have a strong effect on the reduction of TB incidence and mortality, especially in the Indigenous, Black, pardo and extremely poor populations.

Introduction
The COVID-19 pandemic was one of the main drivers of the global increase in extreme poverty and social disparities with devastating effects on Low-and Middle-Income countries (LMICs). 1 Recovery from this crisis has been hampered by high food and energy prices as a result, in part, of the new con icts and by the climate change effects in countries that are major food producers. 2e are currently living in what is de ned as "the polycrisis era", characterised by multiple crises interconnecting and synergizing between them at the global level.Tackling this polycrisis requires greater efforts and investments in social policies to protect the poorest populations, in particular in programs such as conditional cash transfers (CCTs). 2 The health system disruption caused by the COVID-19 pandemic negatively impacted tuberculosis (TB) prevention and care. 1 For the rst time in 2020, the World Health Organization (WHO) announced an upward trend in TB incidence and mortality and a decline in the reported number of people newly diagnosed with TB, 3 a phenomenon that was particularly intense in LMICs 1 and also occurred in Brazil. 4This situation will increase the number of people with undiagnosed and untreated TB, leading to increased transmission of the disease and its severity. 3In particular, the COVID-19 pandemic has raised attention on how deteriorating social determinants of health (e.g., education, poverty, access to healthcare) could have in uenced both TB incidence and mortality and has shown the importance of social protection when responding to global health challenges. 3Social protection, poverty alleviation, and multisectoral actions on broader TB determinants are acknowledged as key pillars of the End TB strategy by 2035, i.e., they are essential to reduce the TB burden. 3nce 2004, Brazil has implemented one of the world's largest CCTs programs, the Bolsa Família Program (BFP).The primary goal of the BFP is to alleviate poverty by providing cash transfers along with requirements related to education and healthcare.The governmental program provides direct cash transfers to poor households with income below the poverty line de ned by the Brazilian government as families earning between United States (US) dollars $18-36 per person per month (at an exchange rate of 5 Brazilian Real to 1 US dollar).The monthly cash bene ts vary from US$17 to a maximum of US$41, depending on the size and composition of the household.In order to continue receiving the bene ts, BFP bene ciaries have to ful l speci c conditionalities related to the healthcare for pregnant women (carrying out prenatal care) and children (compliance with the national vaccination schedule, monitoring nutritional status), and education (school attendance) for children and adolescents (Appendix p.3). 5,6 The BFP has been able to improve the wellbeing of families in poverty, and to reduce social and income inequalities in society, improving access to education, food, and health services. 6Several studies have demonstrated the positive effects of BFP on health outcomes such as child mortality 7 , cardiovascular diseases, 8 suicide, 9 leprosy 10 and some aspects of the TB burden [11][12][13] among others.However, the effects of CCT on TB outcomes among populations at high risk for TB and with limited access to health services, such as marginalised groups and extremely poor populations, 14 have never been systematically estimated.
This study aimed to evaluate the comprehensive effect of the BFP, one of the largest CCT programs in the world, on Tuberculosis incidence, mortality, and case-fatality rates using an unprecedented cohort of 54.5 million low-income Brazilians over 12 years, estimating its heterogeneous effectiveness across the spectrum of ethnoracial factors and socioeconomic conditions.

Study design, population, and ethical issues
This study has a quasi-experimental cohort study design, based on the longitudinal information of 54.5 million individuals from January 1, 2004 to December 31, 2015 (the period for which tuberculosis data were available).First, we constructed a conceptual framework to explain the mechanisms of possible effects of CCT on TB outcomes and to drive the analysis (Figure 1). 15The study population was achieved by selecting a subgroup of individuals of the 100 Million Brazilians Cohort 16 , a consolidated cohort created through the validated linkage 17 between the Federal Government Uni ed Registry for Social Programs (Cadastro Único)that gathers data from the poorest half of the Brazilian population, identifying and characterising low-income families for social programs eligibility, and including information on exposure to the BFP -and health-related datasets from the Brazilian Ministry of Health's (Appendix, p.3).
This study was approved by the Research Ethics Committee of the Institute of Collective Health of the Federal University of Bahia (ISC/UFBA), under number 41691315.0.0000.5030(Assessment nº:3.783.920).

Data sources, outcomes, and intervention
Two individual-level health-related datasets were linked to Cadastro Único (CADU): the Noti able Diseases Information System (SINAN) and the Mortality Information System (SIM).SINAN contains records of noti able diseases, including TB. SIM registers deaths by all causes, according to International Classi cation of Diseases (ICD-10).The linkage codes and algorithms were built based on ve identi ers: date of birth, municipality of residence, sex, name, and mother`s name of the individual in each database.The CADU and the health information datasets (SIM and SINAN) were individually matched in two steps, using the CIDACS-Record Linkage tool (Appendix, p.3).The quality of each link between CADU, SINAN, and SIM has been extensively evaluated and validated. 17An aggregate-level longitudinal dataset -containing a wide range of yearly municipal-level information on TB endemicity levels, municipal infrastructures, and healthcare resources -was also linked to the cohort through the individualsm unicipal code of residence.
Tuberculosis outcomes de ned for the study were: incidence, mortality, and case-fatality rates.The bene ciary group was de ned as eligible individuals who received BFP bene ts, and their exposure started with receipt of the bene t, until the end of their followup.The non-bene ciary group was de ned as individuals who had never bene ted from BFP throughout their follow-up period.In case of non-receipt of the bene ts, eligible individuals were classi ed in the non-bene ciary group (Appendix p.4).

Statistical Analyses
First, in the descriptive analysis, we estimated the rates of the study outcomes as follows: i) TB incidence: new TB diagnoses divided by person-years at risk and multiplied by 100,000; ii) TB mortality: TB deaths, divided by person-years at risk and multiplied by 100,000; and iii) case-fatality rate: TB deaths among people affected by TB, divided by person-years at risk and multiplied by 100.The follow-up time for each individual in the cohort, i.e., person-years, started on the date of entry into the cohort until the date of TB diagnosis (for TB incidence), the date of death due to TB (for TB mortality rate), the date of death from other causes, or the end date of the cohort (December 31, 2015).For TB case-fatality rate, the start date began with the date of diagnosis and ended with the TB-related death, the date of death from other causes, or the nal date of the cohort.Afterwards, we performed a descriptive analysis of new people affected by TB and deaths according to each independent variable.At the individual level, the demographic and socioeconomic covariables were age, sex, self-identi ed race/ethnicity (white, Indigenous, Black and pardothese last categories were analysed together), education, per capita expenditure (as a proxy for the per capita wealth and calculated as a percentage of the yearly minimum wage, categorised by tertiles), and year of entry into the cohort.At the family level, the independent variables were related to household characteristics: number of people, water supply, construction material, sanitation, garbage disposal, and lighting.At the municipal-level, the covariables were unemployment rate, Gini Index, and a set of variables related to health services: Family Health Strategy coverage (the main model of Primary Health Care in Brazil), number of doctors, nurses, and specialised clinics per 1,000 inhabitants.To control for any potential selection bias associating PBF implementation with endemic TB levels in the community, the mean TB incidence rate in the cohort during the study period was included as a covariate in the models.When the study outcome was the case-fatality rate, we also included clinical classi cation of TB, percent of directly observed therapy (DOT), AIDS comorbidity, and diabetes as independent variables.All the variables used in the study are described in the conceptual model (Figure 1).
To estimate the effect of BFP exposure on TB incidence, mortality, and case-fatality rates we used multivariable Poisson regression models, adjusted for all the relevant demographic and socioeconomic confounding variables listed above, with follow-up time as an offset variable, robust standard errors, and observations weighted through stabilised, truncated, inverse probability of treatment weighting (IPTW).0]19 Moreover, in order to understand BFP effects heterogeneity, we tted these IPTW Poisson regression models strati ed by age, sex, race/ethnicity, education, and wealth-tertiles (per capita expenditure).
To con rm the robustness of the ndings, we applied several sensitivity analyses (for details see Appendix, p.9-12): i) we tted models with only individual-level variables and tested the inclusion of different aggregate-level variables, ii) we tted the same regressions without the TB endemicity level variable, iii) we estimated and compared all models without IPTW, iv) to evaluate the adoption of per capita expenses as a proxy for wealth, we carried out the same analyses with other proxies, such as per capita income, v) we adjusted the same models with different speci cations (including different sets of individual-level covariates, inclusion or exclusion of robust standard errors, only in municipalities with adequate vital information).Finally, to have a greater degree of con dence in the causal inference of our impact evaluation, we performed two different triangulation analyses, 20 verifying the existence of BFP effects also using alternative methods: survival analysis with Cox multivariate regression and propensity score matching (PSM) (Appendix, p. 13-14).

Role of the funding source
The funding source had no role in study design, data collection, data analysis, data interpretation, or the writing of the report.

Results
After excluding individuals of the 100 Million Brazilians Cohort who were outside the study period 2004-2015, and who had missing information on demographic or socioeconomic variables, 54,571,434 individuals were selected, of which 23,907,958 were BFP bene ciaries (43.8%), and 30,663,476 non-BFP bene ciaries (56.2%), with a total of 159,777 new TB diagnoses and 7,993 TB deaths (Figure 2).BFP bene ciaries and non-bene ciaries showed similar demographic and socioeconomic characteristics (Table 1).BFP bene ciaries had a slightly higher percentage of people self-identi ed as Black or pardo race/ethnicity, people with no education, households with 3 or more individuals, lesser wealth, without adequate sanitation, and without a public network for water supply (Table 1).TB incidence was lower among BFP bene ciaries than BFP non-bene ciaries (59.07/100,000 person-years at risk [pyr] vs 34.32/100,000 pyr), as well as TB mortality rate (3.99/100,000 person-years at risk [pyr] vs 1.89/100,000 pyr), while TB case-fatality rate was higher among non-bene ciaries (0.68/100,000 pyr vs 1.37/100,000 pyr).
In the strati ed analyses according to wealth tertiles (Table 3), the effect of BFP on TB incidence showed a marked gradient and was considerably stronger among the poorest individuals (aRR:0.49,95%CI:0.49-0.50),gradually decreasing until having only a small effect on the wealthiest individuals (aRR:0.95,95%CI:0.93-0.98).Also, in TB mortality the BFP effect was considerably stronger among the poorest population (aRR:0.60,95%CI:0.55-0.65),and showed a gradient.
In the strati ed analyses according to ethnoracial characteristics, another gradient was evident among Indigenous, Black/pardo, and white people, both for TB incidence (aRR of 0.37 for Indigenous, 0.58 for Black/pardo, 0.67 for white), and TB mortality (aRR of 0.35 for Indigenous, 0.69 for Black/pardo, and 0.83 for white), while the gradient for TB case-fatality rate was not statistically signi cant (Table 3).In terms of education, the effect of BFP on TB incidence was greater in people with less education (aRR of 0.58 vs 0.65 for people with higher education) (Table 3).BFP was also associated with a higher reduction in TB mortality rates in females than in males (aRR of 0.63 vs 0.78, respectively) (Table 3).
All the sensitivity tests con rmed the robustness of the effect estimates, and the triangulation analyses showed a high degree of con dence in the impact evaluation causal inference (Appendix, p.9-15).

Discussion
To the best of our knowledge, this is the largest and most comprehensive impact evaluation of the effects of a Conditional Cash Transfer program on an infectious disease, speci cally Tuberculosis.We systematically analysed, for the rst time, the program's impact among marginalised individuals, evaluating CCT impact according to their ethnoracial and socioeconomic conditions.We observed strong and robust effects of the BFP on decreasing both TB incidence and mortality rates.Notably, BFP effectiveness exhibited a marked gradient based on ethnoracial and socioeconomic conditions, revealing signi cantly stronger effects among Indigenous, Black/pardo, and extremely poor populations.
Our ndings exhibit a clear gradient of BFP effectiveness based on the baseline wealth level of its bene ciaries: BFP shows an extremely strong impact on TB incidence and mortality in extremely poor individuals, while demonstrating a tenfold lower effect on TB incidence in the less poor, with no discernible effects on TB mortality.This pattern is expected, as extreme poverty has been consistently shown to be a signi cant risk factor for the burden of TB, and TB risk levels are known to correlate with poverty levels. 21The BFP, through the direct transfer of money to the poorest and most marginalised families in Brazil, promotes greater access to food, both in quantity and quality, reducing food insecurity and malnutrition, an important TB risk factor, besides improving immune host defences. 6Moreover, housing conditions could improve, reducing crowding and poor ventilation, which are also recognized risk factors for TB. 15 Households could also transition from cooking with burning fuels such as wood, charcoal, coal, and kerosene to cleaner fuels.This change can help reduce indoor air pollution, which has been recognized as a factor contributing to increased TB incidence.Smoking habits and alcoholism, which are strongly associated with poverty, could also decrease among BFP bene ciaries, thereby reducing their chances of being infected with TB.The prevalence of diabetes and HIV/AIDS, which is higher in the most vulnerable individuals, could also be reduced by poverty-reduction interventions. 22onsequently, this reduction may lead to a decrease in the incidence and mortality from TB.Moreover, individuals affected by TB who are living in poverty are more likely to avoid seeking diagnosis at health centres or to interrupt their treatment due to direct costs, such as transportation expenses, and opportunity costs, such as the di culty of missing a day of work due to their precarious employment and subsistence conditions.These costs could represent a barrier even if prevention, diagnosis, and treatment are available free in the Uni ed Health System (SUS). 4 In addition to providing monetary transfers, the conditionalities of the BFP could also contribute to reducing TB.These conditionalities are linked to requirements such as school attendance, education, and access to health services for pregnant women and children under 5 years old, which could facilitate the recognition and early diagnosis of TB. 6 In summary, BFP promotes a greater access to income, food, and healthcare.This may lead to a reduction in TB incidence, 13 facilitate TB early diagnosis and treatment adherence, increase the TB cure rate, 12,21,[23][24][25] reduce complications and death from the disease. 11,13reover, we found that BFP has an extremely strong reduction effect on TB incidence and mortality in Indigenous populations in Brazil, which have a signi cantly higher risk of being infected and dying from TB. 26 While this could be explained by the same mechanisms listed above, particularly in the context of Indigenous populations the receipt of the BFP could alleviate their extreme poverty and socioeconomic vulnerability.This could reduce food insecurity and malnutrition, which are particularly high among Indigenous populations, and lessen the signi cant geographic barriers that hinder access to even basic healthcare services. 27e greater effect of BFP on TB incidence among Black and pardo people is another important nding since more than 60% of new cases of pulmonary TB in Brazil occurred among people who self-identi ed as Black and pardo. 4This could potentially be elucidated by the identical mechanisms detailed earlier.Particularly in the Black and pardo communities in Brazil, the BFP could act on historical and structural social inequities, 28 increasing income and improving education through its conditionalities, providing access to health services and, this way, reducing the TB burden.
TB prevention and care for economically vulnerable populations became an even greater challenge during and after the COVID-19 pandemic. 14Each of these socially vulnerable populations has speci cities and complexities when it comes to implementing TB prevention, diagnosis, and treatment interventions. 14Furthermore, it is important to consider that inequalities linked to local contexts, health facilities, and social, behavioural, and cultural factors, can superimpose on the organisation of health services, in uencing the care provided to these populational groups. 14In this sense, it becomes urgent to intensify the actions of prevention and comprehensive care aimed at people in situations of social vulnerability to TB, as well as intersectoral articulations and the inclusion of the TB on the agendas of Social Assistance and Human Rights, among others.For this purpose, an Inter-Ministerial Committee for the Elimination of Tuberculosis and Other Socially Determined Diseases was recently created in Brazil. 29 the current Brazilian scenario, with an increase in TB cases among socially vulnerable people, the expansion of the BFP can be a bene cial strategy for TB prevention and care. 4It is estimated that the global economy can lose about 1 trillion dollars between 2015 and 2030 due to TB mortality. 30The ndings of the present study reinforce that BFP reduces TB mortality, especially among populations with greater economic and social vulnerability.Thus, the BFP can be an important ally for Brazil to return to the downward trend in TB mortality rates.
Our study has certain limitations that should be acknowledged.Firstly, despite our efforts to control for all relevant confounding variables and utilise propensity score-based models with IPTW, it is important to note that these approaches may not fully account for unobservable confounding variables.To address this concern, we incorporated the average municipal rate of the TB indicator for each outcome under study (incidence, mortality, and case-fatality rate) as independent variables in logistic and Poisson regressions.These rates were estimated among individuals from the same municipalities in the cohort.This allowed us to adjust for baseline levels of the speci c TB outcome under study in the municipalities and potentially associated unobservable variables, controlling for selection biases correlating PBF implementation levels with endemic TB levels in the community.Additionally, other municipal variables were also included as independent variables.Moreover, extensive sensitivity analyses were conducted, which demonstrated that the inclusion of additional independent variables related to socioeconomic inequalities, healthcare services, and municipal infrastructure did not affect the BFP effect estimates (Appendix, p.10).Second, the 100 Million Brazilians Cohort -and our derived study cohort -consists of individuals obtained from the linkage between the Uni ed Registry for Social Programs (CADU) and health data.It is important to note that the data included in our cohort are limited to individuals registered in the CADU, representing the poorest half of the Brazilian population.While our cohort does not include high-income individuals, it does encompass lower middle-income individuals who need to be registered in the CADU to access various governmental assistance programs.However, the external validity of the cohort at the national level is limited.Moreover, although SINAN has high sensitivity in Brazil, there may be underreporting of TB noti cations. 31However, our study design and analytic strategy limits the possibility that underreporting could bias our results.Moreover, results from municipalities selected for high quality of vital information and low undernoti cations, con rm our ndings (Appendix, p. 9).
Our study presents remarkable strengths that contribute to its overall value.Firstly, we leveraged an exceptionally large longitudinal dataset, which, when combined with robust quasi-experimental impact evaluation methods, allowed us to assess the effects of interventions on an unprecedented scale.This unique approach enabled us to include a signi cant number of individuals and subpopulations that are often overlooked or underrepresented in traditional epidemiological studies and randomised controlled trials.This comprehensive inclusion is particularly crucial in policy evaluation, as it highlights the potential differential impacts of public interventions based on the characteristics and baseline conditions of the bene ciaries.Additionally, our study's strength lies in the extensive range of sensitivity analyses conducted.These analyses provided further validation and reinforcement of the study's ndings, ensuring their robustness and reliability.Furthermore, employing various triangulation analyses instilled a high level of con dence in the causal inference of the impact evaluation, further bolstering the credibility of our ndings.

Conclusions
We conclude that Conditional Cash Transfer programs can greatly reduce the incidence and mortality from Tuberculosis, particularly among extremely poor, Indigenous, and Black individuals, who are usually at higher risk for TB and its devastating impacts.Therefore, the expansion of CCT programs in Low and Middle-Income Countries can signi cantly strengthen the global response to TB, reducing social inequalities in the TB burden, and contributing to the achievement of the End TB Strategy and the TB-related Sustainable Development Goals.
Declarations Notes: All models were adjusted for the same demographic and socioeconomic variables in Table 2.

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Figure 1 See
Figure 1

Table 2 .
Estimates of the average effect of the Programa Bolsa Famlília (PBF) adjusted Poisson model (with robust standard errors) on Tuberculosis incidence, mortality and the case-fatality rate in Brazil, 2004-2015.
a The following were used for a comparison between the groups: (i) the two-tailed t-test for continuous.variables and (ii) the Pearson's chi-squared test (χ2) for categorical variables.b SMD -Standardized mean difference.c age categorized every 10 years.d Race or ethnicity: Black/pardo self-declared black or mixed race people.e Proportional to the baseline minimum wage (MW).f Lighting: Non-electric -No meter, lamps, candles, and others.g % of the municipal population with inadequate baseline sanitation.h % of municipality population with baseline garbage collection.i Water supply: Other -well, spring, and others.j PHC percentage coverage.

Table 3 .
of the average effect of the Programa Bolsa Família (PBF), in adjusted Poisson models (with robust standard errors), on Tuberculosis incidence, mortality, and the case-fatality rate inBrazil, 2004-2015.